Inclination parameters of a body, including an inclination angle from a reference, angular velocity, angular acceleration and magnitude of acceleration of the surface of contact, may be used, for example, in robotics applications. Inclination angles are very useful feedback parameters for robotics applications, biomechanical applications (gait analysis), biomedical applications, etc.
In the absence of non-gravity acceleration, a tri-axial accelerometer can be used to measure the inclination angle of a body (e.g. as an inclinometer). For this static case, an algorithm measures the angle between the sensor unit with respect to the direction of the force of gravity. However, this technique will be less accurate when there are relatively large non-gravity accelerations. Furthermore, linear acceleration does not give complete information about all of the inclination parameters.
Gyroscopes are used to measure angular velocity. Strapdown integration algorithms estimate the change in inclination angle by integrating the angular velocity to determine relative movement of the body. The word strapdown indicates that the angular velocity is obtained from the gyroscope strapped onto an object. However, small errors in angular velocity (gyroscope signal) will give rise to large integration errors. Moreover, for measuring absolute inclination parameters as opposed to a change in inclination parameters, reference inclination parameters must to be set.
Orientation estimation is also done by fusion of accelerometer and gyroscope data. Such sensors are called Inertial Measurement Units (IMUs). A conventional IMU practice is to use three single-axis accelerometers and three single-axis gyroscopes aligned orthogonally. The IMUs fuse accelerometer and gyroscope data to estimate inclination parameters. Estimation algorithms, e,g, Kalman Filter, with knowledge of (error) dynamics of the system are used for fusing data from different sensors. The Kalman Filter emphasizes the correctness of linear accelerations when angular accelerations are low, and emphasizes the gyroscope data more when the motion is more dynamic. The IMUs are used in field robotics, assessment of human balancing, space navigation, etc, and use linear accelerations and gyroscope data.
The human body has often been a source of motivation for mechanical design. It has inspired designs of a large number of sensors, including vision, stereo vision, haptics, etc. Mechanically, the human body displays a remarkable quality of maintaining static equilibrium for a body that is in a state of unstable equilibrium (biped stance). The human body is able to maintain equilibrium even when gravity changes (e.g. the moon, etc) and when the surface of contact is accelerating (e.g. an accelerating bus). Interestingly, in such circumstances the equilibrium position of the body changes, for example the body leans forward while sprinting and backward while trying to stop. A filter algorithm particularly for estimating the inclination parameters of human body segments has been researched. The filter uses accelerometer and magnetometer readings to obtain a low frequency component of the inclination parameters and uses the gyroscope for measuring the faster changes in the inclination parameters. However, this may be problematic as the use of magnetometer in the vicinity of ferromagnetic materials will give large errors. Another sensor unit containing a dual-axis fluid inclinometer, a dual-axis electronic compass and tri-axial gyroscope, with a Kalman filter that incorporates a continuous gyroscope offset estimate has been researched.
In humans, the balancing mechanism is based on visual and vestibular feedback to maintain the body in an unstable equilibrium biped stance. Studies show that contributions from visual, joint angle proprioceptive, and force also help in human stance control. Vision improves stance stability, but in principle, can be dispensed with. Biomechanics of the vestibular system has been investigated in detail. The human vestibular system-sensor analogy has always been drawn as single gyroscope and single accelerometer based, or using additional sensor like a magnetometer.
Sensing of inclination parameters allows for using the data to measure joint angles for humanoid robots, gait analysis, fall evaluation, balance prosthesis, etc. Joint angle measurement and inertial sensing is done using camera motion capture systems, combinations of gyroscope-accelerometers-magnetometers.
Camera based motion capture systems are obtrusive and expensive. It is also difficult to be integrated into a modern medical system, such as portable medical device and point-of-care (POC) medication. Alternately, joint angle measurement may utilize magnetic rotary encoders, optical rotary encoders, and/or micro electromechanical sensors (MEMS) including accelerometers and gyroscopes. Magnetic encoders are low-cost, contactless and reliable but require special magnet coupling alignment and magnetic shielding. Optical encoders are very accurate and contactless, but are expensive and are sensitive to environmental influences (shock, vibration, etc.). These sensors must be installed at the joint center, but this may be problematic (or impossible) for some applications, e.g. human knee joints. Small and versatile MEMS related techniques utilize accelerometers and gyroscopes, together with algorithms, in a variety of different ways to estimate inclination parameters of the body. Unlike the conventional sensors, techniques utilizing MEMS accelerometers and/or gyroscopes do not require tight coupling of the sensors to relative mechanical movements, and thus are more flexible at the point of installation, more reliable, and last longer.
Techniques using combinations of gyroscope and accelerometers on robot links can be listed as CMR with gyro-integration (CMRGI), CMR with gyro-differentiation (CMRGD) and distributed CMR (DCMR). The CMRGI and CMRGD use one dual-axis linear accelerometer and one single-axis gyroscope per link for joint angle estimation. The CMRGI integrates the gyroscope signal to estimate the change in inclination angle. This method faces problems due to integration error/drift due to noise. The CMRGD differentiates the gyroscope signal to estimate angular acceleration. It uses the angular acceleration and angular velocity to estimate the acceleration of the joint, then estimates the joint angle. Due to noise, the differentiation of angular velocity to angular acceleration has undesirable errors. The DCMR method uses two dual-axis linear accelerometers per link to estimate the joint angle. Here the difference of the two acceleration signals per link is used to measure the joint angle, thus, avoiding the errors faced by other CMR methods. In this case, the accelerometers have to be theoretically placed along the line joining the two joints, also this method is not able to estimate/measure the base angle (angle of a moving link from the ground e.g. angle of tibia w.r.t gravity when human is walking). Finally, this method does not work when the joint between two links is not revolute.